{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UCI Occupancy Detection dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from pathlib import Path\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "from timeeval import Datasets\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Occupancy Detection and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "dataset_collection_name = \"Occupancy\"\n",
    "source_folder = Path(data_raw_folder) / \"UCI ML Repository/Occupancy Detection\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "from pathlib import Path\n",
    "print(f\"Looking for source datasets in {source_folder.absolute()} and\\nsaving processed datasets in {target_folder.absolute()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset transformation and pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directories /home/projects/akita/data/benchmark-data/data-processed/multivariate/Occupancy already exist\n"
     ]
    }
   ],
   "source": [
    "train_type = \"supervised\"\n",
    "train_is_normal = False\n",
    "input_type = \"multivariate\"\n",
    "datetime_index = True\n",
    "dataset_type = \"real\"\n",
    "\n",
    "# create target directory\n",
    "dataset_subfolder = os.path.join(input_type, dataset_collection_name)\n",
    "target_subfolder = os.path.join(target_folder, dataset_subfolder)\n",
    "try:\n",
    "    os.makedirs(target_subfolder)\n",
    "    print(f\"Created directories {target_subfolder}\")\n",
    "except FileExistsError:\n",
    "    print(f\"Directories {target_subfolder} already exist\")\n",
    "    pass\n",
    "\n",
    "dm = Datasets(target_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_dataset(source: Path, target: Path) -> int:\n",
    "    df = pd.read_csv(source)\n",
    "    df.insert(0, \"timestamp\", pd.to_datetime(df[\"date\"]))\n",
    "    df[\"is_anomaly\"] = df[\"Occupancy\"].astype(int)\n",
    "    df = df.drop(columns=[\"date\", \"Occupancy\"])\n",
    "    df.to_csv(target, index=False)\n",
    "    return len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Occupancy Detection/datatest.txt -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/Occupancy/room-occupancy-0.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Occupancy Detection/datatest2.txt -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/Occupancy/room-occupancy-1.test.csv\n"
     ]
    }
   ],
   "source": [
    "# transform training file\n",
    "dataset_name = \"room-occupancy\"\n",
    "training_filename = f\"{dataset_name}.train.csv\"\n",
    "training_path = os.path.join(dataset_subfolder, training_filename)\n",
    "target_filepath = os.path.join(target_subfolder, training_filename)\n",
    "transform_dataset(source_folder / \"datatraining.txt\", target_filepath)\n",
    "    \n",
    "\n",
    "# get target filenames\n",
    "for i, testfile in enumerate([\"datatest.txt\", \"datatest2.txt\"]):\n",
    "    filename = f\"{dataset_name}-{i}.test.csv\"\n",
    "    source_file = source_folder / testfile\n",
    "    path = os.path.join(dataset_subfolder, filename)\n",
    "    target_filepath = os.path.join(target_subfolder, filename)\n",
    "\n",
    "    # transform file\n",
    "    dataset_length = transform_dataset(source_file, target_filepath)\n",
    "    print(f\"Processed source dataset {source_file} -> {target_filepath}\")\n",
    "\n",
    "    # save metadata\n",
    "    dm.add_dataset((dataset_collection_name, f\"{dataset_name}-{i}\"),\n",
    "        train_path = training_path,\n",
    "        test_path = path,\n",
    "        dataset_type = dataset_type,\n",
    "        datetime_index = datetime_index,\n",
    "        split_at = None,\n",
    "        train_type = train_type,\n",
    "        train_is_normal = train_is_normal,\n",
    "        input_type = input_type,\n",
    "        dataset_length = dataset_length\n",
    "    )\n",
    "\n",
    "dm.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
       "      <th>train_type</th>\n",
       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Occupancy</th>\n",
       "      <th>room-occupancy-0</th>\n",
       "      <td>multivariate/Occupancy/room-occupancy.train.csv</td>\n",
       "      <td>multivariate/Occupancy/room-occupancy-0.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
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       "      <td>supervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>2665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room-occupancy-1</th>\n",
       "      <td>multivariate/Occupancy/room-occupancy.train.csv</td>\n",
       "      <td>multivariate/Occupancy/room-occupancy-1.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>supervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>9752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                       train_path  \\\n",
       "collection_name dataset_name                                                        \n",
       "Occupancy       room-occupancy-0  multivariate/Occupancy/room-occupancy.train.csv   \n",
       "                room-occupancy-1  multivariate/Occupancy/room-occupancy.train.csv   \n",
       "\n",
       "                                                                         test_path  \\\n",
       "collection_name dataset_name                                                         \n",
       "Occupancy       room-occupancy-0  multivariate/Occupancy/room-occupancy-0.test.csv   \n",
       "                room-occupancy-1  multivariate/Occupancy/room-occupancy-1.test.csv   \n",
       "\n",
       "                                 dataset_type  datetime_index  split_at  \\\n",
       "collection_name dataset_name                                              \n",
       "Occupancy       room-occupancy-0         real            True       NaN   \n",
       "                room-occupancy-1         real            True       NaN   \n",
       "\n",
       "                                  train_type  train_is_normal    input_type  \\\n",
       "collection_name dataset_name                                                  \n",
       "Occupancy       room-occupancy-0  supervised            False  multivariate   \n",
       "                room-occupancy-1  supervised            False  multivariate   \n",
       "\n",
       "                                  length  \n",
       "collection_name dataset_name              \n",
       "Occupancy       room-occupancy-0    2665  \n",
       "                room-occupancy-1    9752  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm._df.loc[slice(dataset_collection_name, dataset_collection_name)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2015-02-02 14:19:00</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>26.272000</td>\n",
       "      <td>585.200000</td>\n",
       "      <td>749.200000</td>\n",
       "      <td>0.004764</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2015-02-02 14:19:59</td>\n",
       "      <td>23.718000</td>\n",
       "      <td>26.290000</td>\n",
       "      <td>578.400000</td>\n",
       "      <td>760.400000</td>\n",
       "      <td>0.004773</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2015-02-02 14:21:00</td>\n",
       "      <td>23.730000</td>\n",
       "      <td>26.230000</td>\n",
       "      <td>572.666667</td>\n",
       "      <td>769.666667</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2015-02-02 14:22:00</td>\n",
       "      <td>23.722500</td>\n",
       "      <td>26.125000</td>\n",
       "      <td>493.750000</td>\n",
       "      <td>774.750000</td>\n",
       "      <td>0.004744</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2015-02-02 14:23:00</td>\n",
       "      <td>23.754000</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>488.600000</td>\n",
       "      <td>779.000000</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2800</th>\n",
       "      <td>2015-02-04 10:38:59</td>\n",
       "      <td>24.290000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>1150.250000</td>\n",
       "      <td>0.004829</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2801</th>\n",
       "      <td>2015-02-04 10:40:00</td>\n",
       "      <td>24.330000</td>\n",
       "      <td>25.736000</td>\n",
       "      <td>809.800000</td>\n",
       "      <td>1129.200000</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2802</th>\n",
       "      <td>2015-02-04 10:40:59</td>\n",
       "      <td>24.330000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>817.000000</td>\n",
       "      <td>1125.800000</td>\n",
       "      <td>0.004841</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2803</th>\n",
       "      <td>2015-02-04 10:41:59</td>\n",
       "      <td>24.356667</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>813.000000</td>\n",
       "      <td>1123.000000</td>\n",
       "      <td>0.004849</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2804</th>\n",
       "      <td>2015-02-04 10:43:00</td>\n",
       "      <td>24.408333</td>\n",
       "      <td>25.681667</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>1124.000000</td>\n",
       "      <td>0.004860</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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       "<p>2665 rows × 7 columns</p>\n",
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      "text/plain": [
       "                     date  Temperature   Humidity       Light          CO2  \\\n",
       "140   2015-02-02 14:19:00    23.700000  26.272000  585.200000   749.200000   \n",
       "141   2015-02-02 14:19:59    23.718000  26.290000  578.400000   760.400000   \n",
       "142   2015-02-02 14:21:00    23.730000  26.230000  572.666667   769.666667   \n",
       "143   2015-02-02 14:22:00    23.722500  26.125000  493.750000   774.750000   \n",
       "144   2015-02-02 14:23:00    23.754000  26.200000  488.600000   779.000000   \n",
       "...                   ...          ...        ...         ...          ...   \n",
       "2800  2015-02-04 10:38:59    24.290000  25.700000  808.000000  1150.250000   \n",
       "2801  2015-02-04 10:40:00    24.330000  25.736000  809.800000  1129.200000   \n",
       "2802  2015-02-04 10:40:59    24.330000  25.700000  817.000000  1125.800000   \n",
       "2803  2015-02-04 10:41:59    24.356667  25.700000  813.000000  1123.000000   \n",
       "2804  2015-02-04 10:43:00    24.408333  25.681667  798.000000  1124.000000   \n",
       "\n",
       "      HumidityRatio  Occupancy  \n",
       "140        0.004764          1  \n",
       "141        0.004773          1  \n",
       "142        0.004765          1  \n",
       "143        0.004744          1  \n",
       "144        0.004767          1  \n",
       "...             ...        ...  \n",
       "2800       0.004829          1  \n",
       "2801       0.004848          1  \n",
       "2802       0.004841          1  \n",
       "2803       0.004849          1  \n",
       "2804       0.004860          1  \n",
       "\n",
       "[2665 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_file = source_folder / \"datatest.txt\"\n",
    "df = pd.read_csv(source_file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>140</th>\n",
       "      <td>2015-02-02 14:19:00</td>\n",
       "      <td>23.700000</td>\n",
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       "      <td>585.200000</td>\n",
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       "      <td>0.004764</td>\n",
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       "      <td>23.718000</td>\n",
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       "      <th>142</th>\n",
       "      <td>2015-02-02 14:21:00</td>\n",
       "      <td>23.730000</td>\n",
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       "      <td>572.666667</td>\n",
       "      <td>769.666667</td>\n",
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       "      <td>1</td>\n",
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       "      <th>143</th>\n",
       "      <td>2015-02-02 14:22:00</td>\n",
       "      <td>23.722500</td>\n",
       "      <td>26.125000</td>\n",
       "      <td>493.750000</td>\n",
       "      <td>774.750000</td>\n",
       "      <td>0.004744</td>\n",
       "      <td>1</td>\n",
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       "      <th>144</th>\n",
       "      <td>2015-02-02 14:23:00</td>\n",
       "      <td>23.754000</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>488.600000</td>\n",
       "      <td>779.000000</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>1</td>\n",
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       "      <td>808.000000</td>\n",
       "      <td>1150.250000</td>\n",
       "      <td>0.004829</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2801</th>\n",
       "      <td>2015-02-04 10:40:00</td>\n",
       "      <td>24.330000</td>\n",
       "      <td>25.736000</td>\n",
       "      <td>809.800000</td>\n",
       "      <td>1129.200000</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2802</th>\n",
       "      <td>2015-02-04 10:40:59</td>\n",
       "      <td>24.330000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>817.000000</td>\n",
       "      <td>1125.800000</td>\n",
       "      <td>0.004841</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2803</th>\n",
       "      <td>2015-02-04 10:41:59</td>\n",
       "      <td>24.356667</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>813.000000</td>\n",
       "      <td>1123.000000</td>\n",
       "      <td>0.004849</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2804</th>\n",
       "      <td>2015-02-04 10:43:00</td>\n",
       "      <td>24.408333</td>\n",
       "      <td>25.681667</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>1124.000000</td>\n",
       "      <td>0.004860</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2665 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               timestamp  Temperature   Humidity       Light          CO2  \\\n",
       "140  2015-02-02 14:19:00    23.700000  26.272000  585.200000   749.200000   \n",
       "141  2015-02-02 14:19:59    23.718000  26.290000  578.400000   760.400000   \n",
       "142  2015-02-02 14:21:00    23.730000  26.230000  572.666667   769.666667   \n",
       "143  2015-02-02 14:22:00    23.722500  26.125000  493.750000   774.750000   \n",
       "144  2015-02-02 14:23:00    23.754000  26.200000  488.600000   779.000000   \n",
       "...                  ...          ...        ...         ...          ...   \n",
       "2800 2015-02-04 10:38:59    24.290000  25.700000  808.000000  1150.250000   \n",
       "2801 2015-02-04 10:40:00    24.330000  25.736000  809.800000  1129.200000   \n",
       "2802 2015-02-04 10:40:59    24.330000  25.700000  817.000000  1125.800000   \n",
       "2803 2015-02-04 10:41:59    24.356667  25.700000  813.000000  1123.000000   \n",
       "2804 2015-02-04 10:43:00    24.408333  25.681667  798.000000  1124.000000   \n",
       "\n",
       "      HumidityRatio  is_anomaly  \n",
       "140        0.004764           1  \n",
       "141        0.004773           1  \n",
       "142        0.004765           1  \n",
       "143        0.004744           1  \n",
       "144        0.004767           1  \n",
       "...             ...         ...  \n",
       "2800       0.004829           1  \n",
       "2801       0.004848           1  \n",
       "2802       0.004841           1  \n",
       "2803       0.004849           1  \n",
       "2804       0.004860           1  \n",
       "\n",
       "[2665 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.copy()\n",
    "df1.insert(0, \"timestamp\", pd.to_datetime(df1[\"date\"]))\n",
    "df1[\"is_anomaly\"] = df1[\"Occupancy\"].astype(int)\n",
    "df1 = df1.drop(columns=[\"date\", \"Occupancy\"])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>2015-02-02 22:38:59</td>\n",
       "      <td>20.7</td>\n",
       "      <td>22.456000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>460.600000</td>\n",
       "      <td>0.003385</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>2015-02-02 22:40:00</td>\n",
       "      <td>20.7</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>455.250000</td>\n",
       "      <td>0.003391</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>2015-02-02 22:40:59</td>\n",
       "      <td>20.7</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>455.666667</td>\n",
       "      <td>0.003391</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>2015-02-02 22:41:59</td>\n",
       "      <td>20.7</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>456.250000</td>\n",
       "      <td>0.003391</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>2015-02-02 22:43:00</td>\n",
       "      <td>20.7</td>\n",
       "      <td>22.426667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>459.666667</td>\n",
       "      <td>0.003380</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>2015-02-03 15:14:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>1254.600000</td>\n",
       "      <td>0.005219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636</th>\n",
       "      <td>2015-02-03 15:15:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>461.000000</td>\n",
       "      <td>1261.200000</td>\n",
       "      <td>0.005219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>2015-02-03 15:16:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>464.000000</td>\n",
       "      <td>1261.000000</td>\n",
       "      <td>0.005219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>2015-02-03 15:16:59</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.016667</td>\n",
       "      <td>462.333333</td>\n",
       "      <td>1263.666667</td>\n",
       "      <td>0.005222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>2015-02-03 15:17:59</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.060000</td>\n",
       "      <td>461.000000</td>\n",
       "      <td>1270.200000</td>\n",
       "      <td>0.005229</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  Temperature   Humidity       Light          CO2  \\\n",
       "640   2015-02-02 22:38:59         20.7  22.456000    0.000000   460.600000   \n",
       "641   2015-02-02 22:40:00         20.7  22.500000    0.000000   455.250000   \n",
       "642   2015-02-02 22:40:59         20.7  22.500000    0.000000   455.666667   \n",
       "643   2015-02-02 22:41:59         20.7  22.500000    0.000000   456.250000   \n",
       "644   2015-02-02 22:43:00         20.7  22.426667    0.000000   459.666667   \n",
       "...                   ...          ...        ...         ...          ...   \n",
       "1635  2015-02-03 15:14:00         23.0  30.000000  465.000000  1254.600000   \n",
       "1636  2015-02-03 15:15:00         23.0  30.000000  461.000000  1261.200000   \n",
       "1637  2015-02-03 15:16:00         23.0  30.000000  464.000000  1261.000000   \n",
       "1638  2015-02-03 15:16:59         23.0  30.016667  462.333333  1263.666667   \n",
       "1639  2015-02-03 15:17:59         23.0  30.060000  461.000000  1270.200000   \n",
       "\n",
       "      HumidityRatio  Occupancy  \n",
       "640        0.003385          0  \n",
       "641        0.003391          0  \n",
       "642        0.003391          0  \n",
       "643        0.003391          0  \n",
       "644        0.003380          0  \n",
       "...             ...        ...  \n",
       "1635       0.005219          1  \n",
       "1636       0.005219          1  \n",
       "1637       0.005219          1  \n",
       "1638       0.005222          1  \n",
       "1639       0.005229          1  \n",
       "\n",
       "[1000 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[500:1500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1[[\"Temperature\", \"Humidity\", \"Light\", \"CO2\", \"HumidityRatio\"]].plot()\n",
    "df1[\"is_anomaly\"].plot(secondary_y=True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval",
   "language": "python",
   "name": "timeeval"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
